Systems and methods for optimized design of a supply chain

ABSTRACT

Apparatus, system and method for supply chain management (SCM) system processing. A SCM operating platform is operatively coupled to SCM modules for collecting, storing, distributing and processing SCM data to determine statistical opportunities and risk in a SCM hierarchy. SCM risk processing may be utilized to determine risk values that are dependent upon SCM attributes. Multiple SCM risk processing results may be produced for further drill-down by a user. SCM network nodes, their relation and status may further be produced for fast and efficient status determination.

RELATED APPLICATIONS

The present application claims the benefit of priority to InternationalApplication No. PCT/US2018/033803, filed May 22, 2018, entitled “Systemsand Methods for Optimized Design of a Supply Chain” which claimspriority to, is related to, and incorporates by reference, U.S.provisional application No. 62/509,669, filed May 22, 2017, entitled“Systems and Methods Optimized Design of a Supply Chain”; U.S.provisional application No. 62/509,660, filed May 22, 2017, entitled“Systems and Methods for Risk Processing of Supply Chain ManagementSystem Data”; U.S. provisional No. 62/509,665, filed May 22, 2017,entitled Systems and Methods for Interfaces to a Supply Chain ManagementSystem”; U.S. provisional application No. 62/509,675, filed May 22,2017, entitled Systems and Methods for Assessment and Visualization ofSupply Chain Management System Data; U.S. provisional application No.62/509,653, filed May 22, 2017, entitled Systems and Methods forProviding Diagnostics for a Supply Chain; U.S. patent application Ser.No. 14/523,642, filed Oct. 24, 2014, to Valentine, et al., entitled“Systems and Methods for Risk Processing and Visualization of SupplyChain Management System Data,” which claims priority to U.S. provisionalpatent application Ser. No. 61/895,636, to Valentine, et al., titled“Power Supply With Balanced Current Sharing,” filed Oct. 28, 2013, U.S.provisional patent application Ser. No. 61/895,665, to Joyner et al.,titled “System and Method for Managing Supply Chain Risk,” filed Oct.25, 2013, and U.S. provisional patent application Ser. No. 61/896,251 toMcLellan et al., titled “Method for Identifying and Presenting RiskMitigation Opportunities in a Supply Chain,” filed Oct. 28, 2013. Eachof these is incorporated by reference in their respective entiretiesherein.

BACKGROUND Field of the Disclosure

The present disclosure relates to supply chain management (SCM) systemprocessing. More specifically, the present disclosure is related toprocessing SCM data to reduce cost, optimize data processing andnetworked communications, improving flexibility, and identifying andmitigating risk in a supply chain. Furthermore, the SCM data may bestructured using visualization, analytics and frameworks.

Background of the Disclosure

Supply chains have become increasingly complex, and product companiesare faced with numerous challenges such as globalization, shorteningproduct lifecycles, high mix product offerings and countless supplychain procurement models. In addition, challenging economic conditionshave placed additional pressure on companies to reduce cost to maximizemargin or profit. Focus areas of supply chain-centric companies includereducing cost in the supply chain, maximizing flexibility across thesupply chain, and mitigating risks in the supply chain to prevent lostrevenue.

Supply chain risk, or the likelihood of supply chain disruptions, isemerging as a key challenge to SCM. The ability to identify whichsupplier has a greater potential of a disruption is an important firststep in managing the frequency and impact of these disruptions thatoften significantly impact a supply chain. Currently, supply chain riskmanagement approaches seek to measure either supplier attributes or thesupply chain structure, where the findings are used to compare suppliersand predict disruption. The results are then used to prepare propermitigation and response strategies associated with these suppliers.Ideally, such risk management and assessment would be performed duringthe design of a supply chain for a product or line of products, butdesign tools and data analysis to allow for such design capabilities arenot available in the known art.

Rather than the data- and algorithm-centric supply chain design and riskanalysis discussed above, supply chain risk management is instead mostoften a formal, largely manual process that involves identifyingpotential losses, understanding the likelihood of potential losses,assigning significance to these losses, and taking steps to proactivelyprevent these losses. A conventional example of such an approach is thepurchasing risk and mitigation (PRAM) methodology developed by the DowChemical Company to measure supply chain risks and its impacts. Thisapproach examines supply market risk, supplier risk, organization riskand supply strategy risk as factors for supply chain analysis. Generallyspeaking, this approach is based on the belief that supplier problemsaccount for the large majority of shutdowns and supply chain failures.

Such conventional systems are needlessly complicated and somewhatdisorganized in that multiple layers of classification risks areutilized and, too often, the systems focus mainly on proactivelyendeavoring to predict disruptive events instead of analyzing andprocessing underlying root causes and large-scale accumulated data toassess potential disruptions. Further, these conventional systems failto provide tools to aid in the design of a supply chain at the outset toaddress potential breakdown and disruption, and they also give littleinsight or visibility into the actual supply chain over its entirety.Thus, what is needed is an efficient, simplified SCM processing systemfor aiding in the design of the supply chain, and thereby maximizingopportunities to address potential supply chain risks at the outset andduring the life cycle of a supply chain.

Moreover, conventional supply chain management has historically beenbased on various assumptions that may prove incorrect. By way ofexample, it has generally been understood that the highest risk in thesupply chain resides with suppliers with whom the highest spendoccurs—however, the most significant risk in a supply chain may actuallyreside with small suppliers, particularly if language barriers residebetween the supplier and the supply chain manager, or with sole sourcesuppliers, or in relation to suppliers highly likely to be subject tocatastrophic events, such as earthquakes, for example. Further, it hastypically been the case that increased inventory results in improveddelivery performance—however, this, too, may prove to be an incorrectassumption upon analysis of large-scale data over time and acrossmultiple suppliers, at least in that this assumption is true only if aninventory buffer is placed on the correct part or parts, and at thecorrect service level. Needless to say, such information would bedifficult to glean absent automated review of large-scale data overtime, and without visibility across an entire supply chain.

Yet further, present supply chain management fails to account for muchof the available large-scale data information. By way of example, socialmedia or other third party data sources may be highly indicative ofsupply chain needs or prospective disruptions. For example, if aprovider expresses a desire for increased inventory levels, but socialmedia expresses a largely negative customer sentiment, sales are likelyto fall and the increased inventory levels will likely not be necessary.Similarly, large scale data inclusive of third party data may indicatethat a supplier previously deemed high risk, such as due to the threatof earthquake, is actually lower risk because that supplier has not beenhit with an earthquake over magnitude 5 for that last 20 years, andearthquakes of less than magnitude 5 have only a minimal probability ofaffecting the supply chain in a certain vertical. As such, large scaledata, such as may include social media or other third party data, maycomplement supply chain management in ways not provided by conventionalsupply chain management.

By way of further example, conventional systems often deem certainevents, such as significant geopolitical events, to pose a very highrisk to the supply chain. However, large scale data analysis, such asfrom the inception of the design of many supply chains in a givenvertical and from end-to-end of such supply chains throughout theirrespective life cycles, may reveal that this supposition has generallynot been the case—rather, the supply chain risk may instead be revealedas far more dependent on sole source items and the size and languagespoken by certain suppliers than on geopolitical events, by way ofnon-limiting example.

SUMMARY OF THE DISCLOSURE

Disclosed is an apparatus, system and method for supply chain management(SCM) system processing. A SCM operating platform may be operativelycoupled to SCM modules for collecting, storing, distributing andprocessing SCM data to determine statistical opportunities and risk in aSCM hierarchy. SCM risk processing may be utilized to determine riskvalues that are dependent upon SCM attributes. Multiple SCM riskprocessing results may be produced for further drill-down by a user. SCMnetwork nodes, their relation and status may further be produced forfast and efficient status determination.

More particularly, a supply chain management operating platform isdisclosed for managing a supply chain that includes a plurality ofsupply chain nodes. The platform, and its associated system and method,may include a plurality of data inputs capable of receiving primaryhardware and software data from at least one third party data source andat least one supply chain node upon indication by at least oneprocessor. The platform and its associated system and method may alsoinclude a plurality of rules stored in at least one memory elementassociated with at least one processor and capable of performingoperations on the primary hardware and software data to producesecondary data upon direction from the processor(s). The platform andits associated system and method may also include a plurality of dataoutputs capable of at least one of interfacing with a plurality ofapplication inputs, and capable of providing the secondary data,comprised of at least one of supply chain risk data, supply chainmanagement data, and supply chain analytics, to ones of the plurality ofapplication inputs for interfacing to a user; and interfacing with theuser to provide the secondary data comprised of at least one of supplychain risk data, supply chain management data, and supply chainanalytics.

Further disclosed is a supply chain design and/or redesign platform fordesigning a supply chain comprising a plurality of supply chain nodes.The platform may comprise a plurality of data inputs capable ofreceiving primary hardware and software data from at least one secondsupply chain accessible over a computer network, and capable ofreceiving design data regarding a prospective at least one of theplurality of supply chain nodes, upon indication by at least oneprocessor; a plurality of rules stored in at least one memory elementassociated with the at least one processor and capable of performingcomparative operations on the primary hardware data, the software data,and the design data to produce secondary data upon direction from the atleast one processor; and a plurality of data outputs. The data outputsmay be capable of: providing to a user interface of the secondary datacomprised of at least a substantially optimized one of the design forthe plurality of supply chain nodes based on the comparative applicationof at least ones of the plurality of rules; and providing the secondarydata to a user via the user interface.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example and notlimitation in the figures of the accompanying drawings, in which likereferences indicate similar elements and in which:

FIG. 1 illustrates a computer system for transmitting and processingdata, and particularly supply chain management (SCM) data under anexemplary embodiment;

FIG. 2 illustrates an exemplary processing device suitable for use inthe embodiment of FIG. 1 for processing and presenting SCM data;

FIG. 3A illustrates an exemplary SCM platform

FIG. 3B illustrates the SCM platform utilizing extended plug-inapplications/modules under another exemplary embodiment;

FIG. 4 illustrates exemplary data points and variables modulesoperatively coupled to a SCM platform under one embodiment;

FIGS. 5A-5F illustrate logical processing outcomes for a variety ofexemplary embodiments;

FIG. 6 illustrates an exemplary automation process suitable forutilization in the embodiment of FIG. 1;

FIG. 7 illustrates an exemplary data visualization example foractionable-measurable-proactive SCM processing;

FIG. 8A illustrates a further data visualization and “one-click” reportgeneration under one embodiment;

FIG. 8B illustrates a functional action input module associated withreport generation from the data visualization of FIG. 8A;

FIG. 9 illustrates an exemplary data table providing for attributenaming, attribute description and applicable weight attribution for SCMprocessing;

FIG. 10 illustrates an exemplary risk assembly detail forcommodities/parts, wherein part and supplier attributes are processed todetermine an overall risk;

FIG. 11 illustrates an exemplary risk part detail for commodities/parts,wherein various attributes are processed together with attribute weightsand selection scores to calculate a weighted risk score;

FIG. 12 illustrates an exemplary data visualization heat map for variousassemblies and associated parts, wherein specific assemblies and/orparts are presented as color-coded objects to indicate a level of risk;

FIG. 13 illustrates an exemplary interface for a network optimizer underone exemplary embodiment;

FIG. 14 illustrates an exemplary computing system under the embodiments;

FIG. 15 illustrates an exemplary interface;

FIG. 16 illustrates an exemplary interface;

FIG. 17 illustrates an exemplary interface;

FIG. 18 illustrates an exemplary interface;

FIG. 19 illustrates an exemplary interface;

FIG. 20 illustrates an exemplary interface;

FIG. 21 illustrates an exemplary interface;

FIG. 22 illustrates an exemplary interface;

FIG. 23 illustrates an exemplary interface;

FIG. 24 illustrates an exemplary interface;

FIG. 25 illustrates an exemplary interface;

FIG. 26 illustrates an exemplary interface;

FIG. 27 illustrates an exemplary interface;

FIG. 28 illustrates an exemplary interface;

FIG. 29 illustrates an exemplary interface;

FIG. 30 illustrates an exemplary interface;

FIG. 31 illustrates an exemplary interface;

FIG. 32 illustrates an exemplary interface;

FIG. 33 illustrates an exemplary interface;

FIG. 34 illustrates an exemplary interface;

FIG. 35 illustrates an exemplary interface; and

FIG. 36 illustrates an exemplary interface.

DETAILED DESCRIPTION

The figures and descriptions provided herein may have been simplified toillustrate aspects that are relevant for a clear understanding of theherein described devices, systems, and methods, while eliminating, forthe purpose of clarity, other aspects that may be found in typicaldevices, systems, and methods. Those of ordinary skill may recognizethat other elements and/or operations may be desirable and/or necessaryto implement the devices, systems, and methods described herein. Becausesuch elements and operations are well known in the art, and because theydo not facilitate a better understanding of the present disclosure, adiscussion of such elements and operations may not be provided herein.However, the present disclosure is deemed to inherently include all suchelements, variations, and modifications to the described aspects thatwould be known to those of ordinary skill in the art.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a”, “an” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. The terms “comprises,” “comprising,” “including,” and“having,” are inclusive and therefore specify the presence of statedfeatures, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof. The method steps, processes, and operations described hereinare not to be construed as necessarily requiring their performance inthe particular order discussed or illustrated, unless specificallyidentified as an order of performance. It is also to be understood thatadditional or alternative steps may be employed.

When an element or layer is referred to as being “on”, “engaged to”,“connected to” or “coupled to” another element or layer, it may bedirectly on, engaged, connected or coupled to the other element orlayer, or intervening elements or layers may be present. In contrast,when an element is referred to as being “directly on,” “directly engagedto”, “directly connected to” or “directly coupled to” another element orlayer, there may be no intervening elements or layers present. Otherwords used to describe the relationship between elements should beinterpreted in a like fashion (e.g., “between” versus “directlybetween,” “adjacent” versus “directly adjacent,” etc.). As used herein,the term “and/or” includes any and all combinations of one or more ofthe associated listed items.

Although the terms first, second, third, etc., may be used herein todescribe various elements, components, regions, layers and/or sections,these elements, components, regions, layers and/or sections should notbe limited by these terms. These terms may be only used to distinguishone element, component, region, layer or section from another element,component, region, layer or section. Terms such as “first,” “second,”and other numerical terms when used herein do not imply a sequence ororder unless clearly indicated by the context. Thus, a first element,component, region, layer or section discussed below could be termed asecond element, component, region, layer or section without departingfrom the teachings of the exemplary embodiments.

Computer-implemented platforms, engines, systems and methods of use aredisclosed herein that provide networked access to a plurality of typesof digital content, including but not limited to video, image, text,audio, metadata, algorithms, interactive and document content, and thattrack, deliver, manipulate, transform and report the accessed content.Described embodiments of these platforms, engines, systems and methodsare intended to be exemplary and not limiting. As such, it iscontemplated that the herein described systems and methods may beadapted to provide many types of server and cloud-based valuations,interactions, data exchanges, and the like, and may be extended toprovide enhancements and/or additions to the exemplary platforms,engines, systems and methods described. The disclosure is thus intendedto include all such extensions.

Furthermore, it will be understood that the terms “module” or “engine”,as used herein does not limit the functionality to particular physicalmodules, but may include any number of tangibly-embodied software and/orhardware components having a transformative effect on at least a portionof a system. In general, a computer program product in accordance withone embodiment comprises a tangible computer usable medium (e.g.,standard RAM, an optical disc, a USB drive, or the like) havingcomputer-readable program code embodied therein, wherein thecomputer-readable program code is adapted to be executed by a processor(working in connection with an operating system) to implement one ormore functions and methods as described below. In this regard, theprogram code may be implemented in any desired language, and may beimplemented as machine code, assembly code, byte code, interpretablesource code or the like (e.g., via C, C++, C#, Java, Actionscript,Objective-C, Javascript, CSS, XML, etc.).

Turning to FIG. 1, an exemplary computer system is disclosed in anembodiment. In this example, computer system 100 is configured as a SCMprocessing system, wherein primary processing node 101 is configured tocontain an SCM platform for processing data from other nodes (104, 107),which will be described in further detail below. In one embodiment,primary node 101 comprises one or more servers 102 operatively coupledto one or more terminals 103. Primary node 101 is communicativelycoupled to network 112, which in turn is operatively coupled to supplychain nodes 104, 107. Nodes 104, 107 may be configured as standalonenodes or, preferably, as network nodes, where each node 104, 107comprises network servers 105, 108 and terminals 106, 109, respectively.

As will be explained in the embodiments discussed below, nodes 104, 107may be configured as assembly nodes, part nodes, supplier nodes,manufacturer nodes and/or any other suitable supply chain node. Each ofthese nodes may be configured to collect, store, and process relevantsupply chain-related data and transmit the SCM data to primary node 101via network 112. Primary node 101 may further be communicatively coupledto one or more data services 110, 111 which may be associated withgovernmental, monetary, economic, meteorological, etc., data services.Services 110, 111 may be third-party services configured to providegeneral environmental data relating to SCM, such as interest rate data,tax/tariff data, weather data, trade data, currency exchange data, andthe like, to further assist in SCM processing. Primary node 101 may be“spread” across multiple nodes, rather than comprising a single node,may access data at any one or more of a plurality of layers from nodes104, 107, and may be capable of applying a selectable one or morealgorithms, applications, calculations, or reporting in relation to anyone or more data layers from nodes 104, 107.

FIG. 2 is an exemplary embodiment of a computing device 200 which mayfunction as a computer terminal (e.g., 103), and may be a desktopcomputer, laptop, tablet computer, smart phone, or the like. Actualdevices may include greater or fewer components and/or modules thanthose explicitly depicted in FIG. 2. Device 200 may include a centralprocessing unit (CPU) 201 (which may include one or more computerreadable storage mediums), a memory controller 202, one or moreprocessors 203, a peripherals interface 1204, RF circuitry 205, audiocircuitry 206, a speaker 221, a microphone 222, and an input/output(I/O) subsystem 223 having display controller 218, control circuitry forone or more sensors 216 and input device control 214. These componentsmay communicate over one or more communication buses or signal lines indevice 200. It should be appreciated that device 200 is only one exampleof a multifunction device 200, and that device 200 may have more orfewer components than shown, may combine two or more components, or amay have a different configuration or arrangement of the components. Thevarious components shown in FIG. 2 may be implemented in hardware or acombination of hardware and tangibly-embodied, non-transitory software,including one or more signal processing and/or application specificintegrated circuits.

Data communication with device 200 may occur via a direct wired link ordata communication through wireless, such as RF, interface 205, orthrough any other data interface allowing for the receipt of data indigital form. Decoder 213 is capable of providing data decoding ortranscoding capabilities for received media, and may also be enabled toprovide encoding capabilities as well, depending on the needs of thedesigner. Memory 208 may also include high-speed random access memory(RAM) and may also include non-volatile memory, such as one or moremagnetic disk storage devices, flash memory devices, or othernon-volatile solid-state memory devices. Access to memory 208 by othercomponents of the device 200, such as processor 203, decoder 213 andperipherals interface 204, may be controlled by the memory controller202. Peripherals interface 204 couples the input and output peripheralsof the device to the processor 203 and memory 208. The one or moreprocessors 203 run or execute various software programs and/or sets ofinstructions stored in memory 208 to perform various functions for thedevice 200 and to process data including SCM data. In some embodiments,the peripherals interface 204, processor(s) 203, decoder 213 and memorycontroller 202 may be implemented on a single chip, such as a chip 201.In some other embodiments, they may be implemented on separate chips.

The RF (radio frequency) circuitry 205 receives and sends RF signals,also known as electromagnetic signals. The RF circuitry 205 convertselectrical signals to/from electromagnetic signals and communicates withcommunications networks and other communications devices via theelectromagnetic signals. The RF circuitry 205 may include well-knowncircuitry for performing these functions, including but not limited toan antenna system, an RF transceiver, one or more amplifiers, a tuner,one or more oscillators, a digital signal processor, a CODEC chipset, asubscriber identity module (SIM) card, memory, and so forth. RFcircuitry 205 may communicate with networks, such as the Internet, alsoreferred to as the World Wide Web (WWW), an intranet and/or a wirelessnetwork, such as a cellular telephone network, a wireless local areanetwork (LAN) and/or a metropolitan area network (MAN), and otherdevices by wireless communication. The wireless communication may useany of a plurality of communications standards, protocols andtechnologies, including but not limited to Global System for MobileCommunications (GSM), Enhanced Data GSM Environment (EDGE), high-speeddownlink packet access (HSDPA), wideband code division multiple access(W-CDMA), code division multiple access (CDMA), time division multipleaccess (TDMA), BLE, Bluetooth, Wireless Fidelity (Wi-Fi) (e.g., IEEE802.11a, IEEE 802.11b, IEEE 802.11g and/or IEEE 802.11n), voice overInternet Protocol (VoIP), Wi-MAX, a protocol for email (e.g., Internetmessage access protocol (IMAP) and/or post office protocol (POP)),instant messaging (e.g., extensible messaging and presence protocol(XMPP), Session Initiation Protocol for Instant Messaging and PresenceLeveraging Extensions (SIMPLE), and/or Instant Messaging and PresenceService (IMPS)), and/or Short Message Service (SMS)), or any othersuitable communication protocol, including communication protocols notyet developed as of the filing date of this document.

Audio circuitry 206, speaker 221, and microphone 222 may provide anaudio interface between a user and the device 200. Audio circuitry 1206may receive audio data from the peripherals interface 204, converts theaudio data to an electrical signal, and transmits the electrical signalto speaker 221. The speaker 221 converts the electrical signal tohuman-audible sound waves. Audio circuitry 206 also receives electricalsignals converted by the microphone 221 from sound waves, which mayinclude audio. The audio circuitry 206 converts the electrical signal toaudio data and transmits the audio data to the peripherals interface 204for processing. Audio data may be retrieved from and/or transmitted tomemory 208 and/or the RF circuitry 205 by peripherals interface 204. Insome embodiments, audio circuitry 206 also includes a headset jack forproviding an interface between the audio circuitry 206 and removableaudio input/output peripherals, such as output-only headphones or aheadset with both output (e.g., a headphone for one or both ears) andinput (e.g., a microphone).

I/O subsystem 223 couples input/output peripherals on the device 200,such as touch screen 215 and other input/control devices 217, to theperipherals interface 204. The I/O subsystem 223 may include a displaycontroller 218 and one or more input controllers 220 for other input orcontrol devices. The one or more input controllers 220 receive/sendelectrical signals from/to other input or control devices 217. The otherinput/control devices 217 may include physical buttons (e.g., pushbuttons, rocker buttons, etc.), dials, slider switches, joysticks, clickwheels, and so forth. In some alternate embodiments, input controller(s)220 may be coupled to any (or none) of the following: a keyboard,infrared port, USB port, and a pointer device such as a mouse, anup/down button for volume control of the speaker 221 and/or themicrophone 222. Touch screen 215 may also be used to implement virtualor soft buttons and one or more soft keyboards.

Touch screen 215 provides an input interface and an output interfacebetween the device and a user. The display controller 218 receivesand/or sends electrical signals from/to the touch screen 215. Touchscreen 215 displays visual output to the user. The visual output mayinclude graphics, text, icons, video, and any combination thereof(collectively termed “graphics”). In some embodiments, some or all ofthe visual output may correspond to user-interface objects. Touch screen215 has a touch-sensitive surface, sensor or set of sensors that acceptsinput from the user based on haptic and/or tactile contact. Touch screen215 and display controller 218 (along with any associated modules and/orsets of instructions in memory 208) detect contact (and any movement orbreaking of the contact) on the touch screen 215 and converts thedetected contact into interaction with user-interface objects (e.g., oneor more soft keys, icons, web pages or images) that are displayed on thetouch screen. In an exemplary embodiment, a point of contact between atouch screen 215 and the user corresponds to a finger of the user. Touchscreen 215 may use LCD (liquid crystal display) technology, or LPD(light emitting polymer display) technology, although other displaytechnologies may be used in other embodiments. Touch screen 215 anddisplay controller 218 may detect contact and any movement or breakingthereof using any of a plurality of touch sensing technologies now knownor later developed, including but not limited to capacitive, resistive,infrared, and surface acoustic wave technologies, as well as otherproximity sensor arrays or other elements for determining one or morepoints of contact with a touch screen 215.

Device 200 may also include one or more sensors 216 such as opticalsensors that comprise charge-coupled device (CCD) or complementarymetal-oxide semiconductor (CMOS) phototransistors. The optical sensormay capture still images or video, where the sensor is operated inconjunction with touch screen display 215. Device 200 may also includeone or more accelerometers 207, which may be operatively coupled toperipherals interface 1204. Alternately, the accelerometer 207 may becoupled to an input controller 214 in the I/O subsystem 211. Theaccelerometer is preferably configured to output accelerometer data inthe x, y, and z axes.

In one embodiment, the software components stored in memory 208 mayinclude an operating system 209, a communication module 210, atext/graphics module 211, a Global Positioning System (GPS) module 212,audio decoder 1213 and applications 214. Operating system 209 (e.g.,Darwin, RTXC, LINUX, UNIX, OS X, Windows, or an embedded operatingsystem such as VxWorks) includes various software components and/ordrivers for controlling and managing general system tasks (e.g., memorymanagement, storage device control, power management, etc.) andfacilitates communication between various hardware and softwarecomponents. A SCM processing platform may be integrated as part ofoperating system 209, or all or some of the disclosed portions of SCMprocessing may occur within the one or more applications 214.Communication module 210 facilitates communication with other devicesover one or more external ports and also includes various softwarecomponents for handling data received by the RF circuitry 205. Anexternal port (e.g., Universal Serial Bus (USB), Firewire, etc.) may beprovided and adapted for coupling directly to other devices orindirectly over a network (e.g., the Internet, wireless LAN, etc.).

Text/graphics module 211 includes various known software components forrendering and displaying graphics on a screen and/or touch screen 215,including components for changing the intensity of graphics that aredisplayed. As used herein, the term “graphics” includes any object thatcan be displayed to a user, including without limitation text, webpages, icons (such as user-interface objects including soft keys),digital images, videos, animations and the like. Additionally, softkeyboards may be provided for entering text in various applicationsrequiring text input. GPS module 212 determines the location of thedevice and provides this information for use in various applications.Applications 214 may include various modules, including addressbooks/contact list, email, instant messaging, video conferencing, mediaplayer, widgets, instant messaging, camera/image management, and thelike. Examples of other applications include word processingapplications, JAVA-enabled applications, encryption, digital rightsmanagement, voice recognition, and voice replication. Under oneembodiment, a 3D object may have access to any or all of features inmemory 208.

Turning to FIG. 3A, a SCM operating platform 307 is disclosed, whereinplatform 307 may reside at a primary node 101. Platform 307 may beconfigured to perform and/or control SCM data processing on datareceived from external nodes 104, 107 and other data sources 110, 111.Platform 307 is operatively coupled to control module 302, which may beconfigured to process, connect and visualize nodes and their respectivegeographic locations. Network optimization module 303 processes SCM datato determine which nodes and links meet or exceed predetermined riskthresholds and determines new nodes and/or links that may be added,deleted and/or substituted to establish more efficient networkoptimization.

Supply chain analytics module 304 may be configured to process incomingsupply chain data and forward results to platform 307 for storage,distribution to other modules and/or for further processing. Each ofmodules 302-306 may share data between themselves via platform 307.Platform 307 may further be configured to generate visualizations, suchas media, charts, graphs, node trees, and the like, for inspectionand/or follow-up action by a user.

The platform of FIG. 3A is configured to utilize extensive data acrossmany primary and secondary nodes, advanced analytics, logic andvisualization to convert extensive, voluminous unstructured data into aneasy-to-action, prioritized list of tasks for improved SCMfunctionality. One advantageous effect of the platform is that it iseffective in identifying actual and potential opportunities ofimprovement, such as based on analysis of extended historical data ofsimilar or related supply chains. These opportunities are designed tostreamline and optimize SCM by generating better SCM terms, models andimplementation of optimal parameter settings.

FIG. 3B illustrates, at the primary node 101 of a data exchange diagram,platform 307. In the illustration, platform 307 may provide a pluralityof rules and processes, such as the aforementioned analytics, exceptionmanagement, risk management, and visualization techniques, that may beapplied by one or more modules. That is, access to the rules andprocesses provided by the platform may be available to theaforementioned modules. Thus, these applications, also referred toherein as “apps” or modules, may be “thin client”, wherein the processesreside entirely within the platform's processing and are accessed by theapp; “thick client,” wherein the processes reside entirely within theapp's processing; or partially thin client, wherein processing and ruleapplication is shared between the app and the platform.

Data inputs for the one of more modules, also referred to in thepertinent art as “data hooks” for “apps,” may be associated with theplatform 307, and thus may obtain data that is made available by theplatform, such as may be obtained from hardware or software outputsprovided from nodes 104, 107 and/or sources 110, 111. As illustrated,data may be received in platform modules for risk management 311,analytics 312, information visualization 313 and exception management314. Output data from any given app may be provided throughvisualization rules unique to the app and within the app, or via theplatform, such as within a discreet display aspect for a given appwithin the platform. Output data from any given app may be provided,such as through visualization rules unique to the app, within the app,or via the platform, such as within a discreet display aspect, such as adrop down, top line, or side line menu, for a given app within theplatform.

Moreover, primary data employed by the platform and its associated appsmay be atypical of that employed by conventional SCM systems. Forexample, customer intelligence data may include social media trendsand/or third party data feeds in relation to a supply chain, or for allsupply chains for similar devices, device lines, or for device linesincluding the same or a similar part. Secondary data derived from thethird party data sources for a device, for example, allows for secondarydata to be derived therefrom in relation to inventory stock, the needfor alternate sourcing, and the like. For example, a negative overallindication on a device, as indicated by social media data drawn from oneor more networked social media locations, would indicate a need fordecreased inventory (since a negative consumer impression likelyindicates an upcoming decrease in sales), notwithstanding any request bythe seller of the device to the contrary. This need for decreasedinventory may also dictate modifications for the presently disclosed SCMof the approach to other aspects of the supply chain, such as partsneeded across multiple customers, the need to de-risk with multiplesources for parts, the need to ship present inventory in a certaintimeframe, and the like. This same data may be mined for other purposes,such as to assess geopolitical, weather, and like events.

The disclosure thus provides a SCM operating platform 307 suitable forreceiving base data from the supply chain, and/or from a data store,and/or from third party networked sources, and applying thereto aplurality of rules, algorithms and processes to produce secondary data.This secondary data may be made available within the platform, and/ormay be made available to one or more apps, to provide indications to theuser based on the applied rules, algorithms and processes. Therefore,the disclosure makes use of significant amounts of data across what maybe thousands of supply chain nodes for a single device line to allow forsupply chain management, risk management, supply chain monitoring, andsupply chain modification, in real time. Moreover, based on thesignificant data available to the platform, the platform and/or itsinterfaced apps may “learn” from certain of the data received, such astrend data fail point data, or the like, in order to modify theaforementioned rules, algorithms and processes, in real time and forsubsequent application.

Because the apps disclosed make use of the data, rules, algorithms, andprocesses provided by the platform, any number of different componentapps may be provided. Apps may interface with the platform solely toobtain data, and may thereafter apply unique app-based rules, algorithmsand processes to the received data; or apps may make use of the data andsome or all of the rules, learning algorithms, and processes of theplatform and may solely or most significantly provide variations in thevisualizations regarding the secondary data produced. Those skilled inthe art will thus appreciate, in light of the instant disclosure, thatvarious of the apps and data discussed herein throughout are exemplaryonly, and thus various other apps, data input, and data output may beprovided without departing from the spirit or scope of the invention.

Turning now to FIG. 4, an embodiment is illustrated for a materialssystem utilizing the platform 307 of FIG. 3. As SCM data is entered intothe system, various data points, variables and loads are entered intothe SCM system database for processing and/or distribution to any of thevarious modules described herein. For each node, a hierarchy structure401 is determined, which may comprise one or more sites 401A, customergroups 401 B, customers 401C, region 401 D, division 401E and sector 401E. It is understood by those skilled in the art that the hierarchicalstructure data points may include additional, other, data points, or maycontain fewer data points as the case may be.

Other entries in the embodiment of FIG. 4 include part number 402, whichmay comprise unique customer material component numbers for each part.Stock on hand 403 may comprise data relating to a current quantity ofeach component in stock by ownership. For example, quantity data may besegregated among manufacturers, suppliers and customers. It may beunderstood by those skilled in the art, in light of the instantdisclosure, that other entries and segregations are contemplated by thepresent disclosure. For example, data may also be segregated amongtypes, such as Raw, Work In Process (WIP) and Finished Goods (FG). Datamay likewise be segregated by location, such as by Warehouse,Manufacturing Line, Test, Packout, Shipping, etc., or by using any othermethodology that may be contemplated by the skilled artisan in view ofthe discussion provided herein.

Unit price 404 may contain data relating to a cost per component. Thecost may be determined via a materials cost, labor cost, or somecombination. ABC classification 405 may comprise a classification valueof procurement frequency (e.g., every 7 days, 14 days, 28 days, etc.).

ABC Analysis is a term used to define an inventory categorizationtechnique often used in materials management. It is also known asSelective Inventory Control. Policies based on ABC analysis aretypically structured such that “A” items are processed under very tightcontrol and accurate records, “B” items are processed under less tightlycontrolled and good records, and “C” items are processed under thesimplest controls possible and minimal records. ABC analysis provides amechanism for identifying items that will have a significant impact onoverall inventory cost, while also providing a mechanism for identifyingdifferent categories of stock that will require different management andcontrols. ABC analysis suggests that inventories of an organization arenot of equal value. Thus, the inventory is grouped into three categories(A, B, and C) in order of their estimated importance. Accordingly, “A”items are very important for an organization. Because of the high valueof these “A” items, frequent value analysis is required. In addition tothat, an organization needs to choose an appropriate order pattern (e.g.“just-in-time”) to avoid excess capacity. “B” items are important, butless important than “A” items and more important than “C” items(marginally important). Accordingly, “B” items may be intergroup items.ABC type classifications within the system may help dictate how oftenmaterials are procured. By way of non-limiting example, to limit thevalue of inventory holding and risk, A Classes may be predominantlyordered once per week, B Classes bi-weekly, and C Classes monthly.

MOQ 406 may comprise data relating to a component minimum order quantityfor a predetermined time period. This value may be advantageous indetermining, for example, a minimum order quantity that must be procuredover a predetermined time period. Multiple 407 may comprise datarelating to component multiple quantities, such as multiples for demandmore than the MOQ value. System lead time 408 may comprise data relatingto a system period of time required to release a purchase order prior toreceiving components.

Continuing with the example of FIG. 4, supplier 417 may compriseidentity data relating to a component source supplier. SourcingApplication Tool (SAT) lead time 409 may comprise a supplier quotedperiod of time required to release a purchase order prior to receivingcomponents. Safety stock 410 may comprise component and FG buffer stockdata relating to a buffer stock quantity that will be excluded fromavailable stock until a shortage status is detected. Safety lead time411 may comprise component buffer lead time data that may be utilized torecommend a component be delivered in an on-time orearlier-than-expected manner. Quota percentage 412 may comprise datarelating to a percentage of supply which should be allocated to eachcomponent source supplier. Supply 414 may comprise data relating tosupply quantity per component over a predetermined (e.g., 90-day) timeperiod. 90-day demand 416 may comprise data relating to customer demandquantity per component over a 90-day time period. Manufacturers of oneor more components may also be tracked separately from suppliers. Insome cases, a supplier may act as a distributor by stocking parts fromdifferent manufacturers to make them quickly available, but typically ata higher price. Here, supply chain models, such as consigned materialand/or vendor managed processes, may be used to assist in identifyingand potentially offsetting the extra cost.

Utilizing the exemplary platform illustrated in FIGS. 3 and FIG. 4, anumber of advantageous SCM processing determinations may be made. In oneexample, optimal values or actions may be generated based onpredetermined logic. For example, a MOQ may be determined to be 10,000units, while an optimal MOQ quantity is calculated to be 6,000 units.Calculating an area of improvement based on predefined logic, thereduction of current MOQ may be calculated to be 4,000 units(10,000-6,000). Data values calculated for improvement may be determinedaccording to predefined logic, wherein for the exemplary MOQ improvementof 4,000 units, a unit value multiplier of $1 would yield an improvement“opportunity” value of $4,000. As used herein, the phrase “opportunityvalue” may be used to indicate a particular area of data, such as anitem source, a replacement part, an inventory level, or the like, thatprovides an opportunity to improve an indicated area of the supplychain, such as de-risking, lowering costs, increasing available sources,optimizing inventory levels, or the like.

In addition, one or more opportunity thresholds may be set for eachcomponent, and a resulting prioritization may be determined. Forexample, the system may be configured to only list components with anopportunity value greater than $1,000, where opportunities are sorted ina descending value. Ownership of each component may be assigned, wherethe system may notify users associated with an ownership entity. Eachcomponent may be assigned to multiple users or a single user, andlargest opportunities may be identified and notified first. Owners mayassign actions, add comments, and potentially escalate SCM data. Forexample, owners may advise which actions have been taken or escalatedata for resolution, etc. When all options and/or system negotiationsare completed or exhausted, the system may manually or automaticallyclose a SCM task associated with the data.

In the field of SCM processing, various data points have been used toimprove a supply chain. However, the present applicants have identifieda number of data areas that are relatively efficient to obtain andprocess. These data areas are opportunity value areas that havepotentially been overlooked by conventional approaches, but have beenfound to be useful in determining better days in inventory, inventoryturn and cash flow, among others. One data area includes MOQ, whichprovides opportunities to reduce MOQ to an optimal quantity using logicbased on order frequency, multiple quantities and demand profiles.Another data area includes safety stock, which provides opportunities toreduce safety stock levels using logic based on order frequency anddemand profiles to an optimal safety stock (buffer quantity). Yetanother data point includes lead time, which may provide opportunitiesto reduce procurement lead times with higher system parameters versus anactive quotes database.

A still further data point includes safety lead time, which may provideopportunities to reduce safety lead times based on removal of systemparameters and/or reducing excessive parameters. Excess inventory datapoints may also provide opportunities to reduce owned excess inventorybased on a rolling measurement and highlight supplier returnsprivileges. Supply not required data points may also provide opportunityto reduce, divert, cancel, etc., material arriving within a certainperiod which is not required to meet customer demand. Of course theaforementioned data points are not exclusive and may be combined withother data points discussed in the present disclosure or other datapoints known in the art.

Thus, for example and as further illustrated with regard to FIGS. 5A-5F,6-7, and 8A and B, described below, derived secondary data may beprovided to indicate, for example, a recommended buffer for aninventoried part. A risk calculation, as discussed in more detail belowwith regard to FIGS. 9-12, may indicate that a particular part is a highrisk part (such as because it is from a small, sole source, foreignsupplier). Further, as is often the case with a high risk part, theindication may be that the part is relatively inexpensive in relation toother parts for a given device. Consequently, the presently disclosedSCM platform 307, notwithstanding a calculation that the optimalprocurement time may be 14 days, may derive secondary data from thecombinations of the optimal procurement secondary data, the riskassociated with the part, and the cost of the part, that a 28 day buffershould be ordered for the part at each of the next two 14 dayprocurement windows—thereby increasing the buffer for this key, highrisk part using the learning algorithms of the platform 307. That is,the disclosed embodiments may perform balancing of input primary andderived secondary data to arrive at a solution that is optimal whenconsidering a wide range of factors, but which is not necessarilyoptimal for any given factor.

FIGS. 5A-5F illustrates various examples of data processing undervarious embodiments. FIG. 5A provides an exemplary process based on MOQdata points. In this example, the logic is to process an ABCclassification, indicating how often a component is procured. Forexample, an “A” class part may be procured every 7 days, class “B” 14days and class “C” 28 days. The MOQ and multiple (Pack Size) data pointsare then processed in the system over a predetermined time period (e.g.,90 days). Referring to FIG. 5A, the system calculates a 7 day-of-service(DOS) quantity of 5,911 based on a daily forward looking demand of 844.If the system is configured to have a DOS threshold of 7 days, therespective multiple quantity of 1,000 may be rounded up to greater thanthe 7 DOS quantity, which results in a new MOQ of 6,000 (6×multiple qty.1,000). Here, the supplier may be notified to reduce MOQ to 6,000, asthe purchaser will not want to purchase more than 7 DOS for a class “A”part. Furthermore, if a multiple quantity is a genuine pack size, thenthe purchaser may be able to purchase 6 units instead of 10. Since theunit reduction is calculated to be 4,000 (10,000−6,000), the reductionvalue may be multiplied by the unit value to determine a totalopportunity value. This calculation in turn may be used by the system toeffect monthly/quarterly ending inventory values. Such a configurationadvantageously improves end-of-life situation and reduce liabilitywithin a supply chain.

Turning to FIG. 5B, the exemplary embodiment provides an illustrativeprocess based on safety stock data points. In this example, the systemdetermines if a safety stock is available, and, if one is available, asimilar ABC and daily demand logic described above is applied. The dailydemand quantity is used to calculate what seven, fourteen and 28 DOSsshould be, and, if the part safety stock quantity is greater than thisvalue, then a new safety stock quantity is established. In one example,class “A” part of 5,911 quantity is determined to have a 7 DOS.Accordingly, the system automatically sets a new SS quantity at 5,911.One reason for this is that, for class “A” parts, a location should notbe holding more than 7 days safety stock, (unless otherwise configuredby the system), as this pulls the full order book from suppliers earlyand may affect ending inventory values. In the example of FIG. 5B, thesame process may be repeated for “B” and “C” classes for 14 and 28 daytime periods, respectively.

FIG. 5C illustrates an exemplary embodiment using lead time data points.Lead time data is more straightforward to process, where the systemsimply takes a SAT quote for the part and supplier combination. If thesystem finds a quote and the lead time is less than what is entered inthe system, additional processing steps may be taken. First, dailydemand quantity is calculated, and, based on this, the system uses thedifference between the current lead time and the SAT lead time tocalculate an opportunity value. As an example, assuming a unit cost is$1, and a lead time reduction is 84 days in SAP versus 70 days in SAT(14 days), the 14 day reduction may be processed with a daily demandvalue such that 844×$1=$844×14 days=$11,822.

In FIG. 5D, an exemplary embodiment is provided using safety lead timedata points. Similar to lead time, safety lead time is straightforwardfor the system to process, where the system looks for the removal of thefull SLT if the SLT indicator is set. In this case, the systemcalculates a daily demand quantity, and, based on this, it uses areduction of a current SLT to calculate an opportunity value. Forexample, assuming a unit cost is $1, and the SLT is 10 days in SAP, thedaily demand value (844×$1=$844) is multiplied by the reduction of 10days, which results in $8,444.

In FIG. 5E, an exemplary embodiment is illustrated for the systemutilizing excess stock data points. Here the owned stock on hand iscompared to the next 90 days of demand. If the stock is greater than thespecified demand, then the remaining quantity is then used as an excessquantity. As such, an opportunity value may be calculated, based on aproduct of the standard unit cost. For example, if 150,000 units ofstock is on hand, and the next 90 days demand (which may include pastdemand) is only 76,000, the system would process the data such that150,000−76,000=74,000. Again assuming a unit cost of $1, the opportunityvalue may be determined to be $74,000. In one embodiment, the system mayalso highlight is the part/unit has potential supplier returnsprivileges in place via a connection to SAT.

In FIG. 5F, a supply not required data point processing embodiment isshown. In this example, certain supplier purchase orders are notrequired to meet current quarter demands. Here, the opportunity value isnot necessarily limited the purchase order quantity (e.g., useexception); the system calculates a quantity arriving within a quarterwhich is greater that a needed quantity. For example, assuming a unitcost is $1 and the stock on hand is 100,000, the demand unit quarter endis 12,000 and a supply unit QTR end is 50,000. Here the system wouldprocess the data points as (100,000+50,000)−120,000=30,000 (or $30,000that is not required, representing $30,000 of opportunity)

In addition to the examples provided in FIGS. 5A-F, other variables maybe utilized by the system for optimization. For example, masterproduction schedule (MPS) tactical rules may be employed to generate ascorecard format in order to identify areas of concern and opportunity.By using a plurality of variables as inputs, an MPS may be configured togenerate a set of outputs for decision making within the system. Inputsmay include any of the data points disclosed herein, as well as forecastdemand, production costs, inventory money, customer needs, inventoryprogress, supply, lot size, production lead time, and capacity. Inputsmay be automatically generated by the system by linking one or moredepartments at a node with a production department. For instance, when asale is recorded, the forecast demand may be automatically shifted tomeet the new demand. Inputs may also be inputted manually from forecaststhat have also been calculated manually. Outputs may include amounts tobe produced, staffing levels, quantity available to promise, andprojected available balance. Outputs may be used to create a MaterialRequirements Planning (MRP) schedule.

Other variables may include product lead-time stack data, whereprocurement and manufacturing lead times are stacked (which may alsoinclude safety lead times) to an end product level to identify areas ofconcern and opportunity. Supply chain models may also be used tooptimize inventory. For example, if a sub-optimal supply chain model isnot in place with current suppliers, arrangements with customers may beprocessed to improve the supply chain model, which in turn could allowfor identification and/or quantification for potential inventoryreduction or inventory avoidance opportunities. Supplier payment termdata may also be cross-referenced to identify potential extended paymentterms to produce better cash flow.

Generally speaking, certain features and processes described herein arebased on a “plan-do-check-act” (PDCA) methodology, where the PDCA cyclemay be thought of as a checklist of multiple stages to solve SCM issues.The methodology described above may effectively be used to identifyopportunities, and, when no suitable opportunities are available, cyclethe system to flag the lack of opportunity and move to another suitablearea.

In FIG. 6, an exemplary block diagram of an automatic AMP process isillustrated, where a supply chain dashboard and scorecard for SCM datais generated by the system in 601 and forwarded to automatedsubscription 602. In certain instances when a process cannot beautomated, a manual run and export function 603 may be provided. SCMdata may then be processed in a supply chain development manager (SCDM)module/global planning manager (GPM) module that may be part of thesystem platform. The modules allow for business team analytics andreview, where part ownership is assigned and used to provide one or moresummary/detail reports issued at predetermined times (e.g., weekly).Once the system has reviewed the relevant data, a process ownerutilizing the system may drive action for subsequentnegotiation/implementation 609. In instances where unresolved issuesarise, an escalation process may flag the issue for higher level systemreview. As processes are completed (or left unresolved), the systemcloses the current process.

In addition to data processing, the SCM platform system advantageouslypackages processed data to be uniquely visualized on a user's screen. Inthe example of FIG. 7, an exemplary bubble chart 700 is illustrated,where a total opportunity visualization is provided using MOQ, lead timeand safety stock data points. Here, various opportunities identified inthe system relating to MOQ using any of the techniques described herein.The various identified opportunities are visualized in the system as“bubbles” of varying size 702, where the size of the bubble is dependentupon the size of the opportunity. In this example, MOQ opportunity 701is identified as having the largest opportunity ($2M). The remainingbubbles in the exemplary illustration, as well as in certain otherexamples disclosed herein, may, of course, represent other opportunitiesavailable.

Similarly, lead time opportunities identified by the system arevisualized 704, where lead-time opportunity 703 is identified as thelargest opportunity ($1 M). Likewise, safety stock opportunities 706 areidentified and opportunity 705 is identified as the largest opportunity($5M). As each of the largest opportunities are identified (701, 703,705), they are linked to total opportunity bubble 707 which visualizes atotal opportunity value ($8,550,323). The system may be configured suchthat, as other opportunities (i.e., opportunities other than thelargest) are selected, the total opportunity bubble 707 automaticallyrecalculates the total opportunity value for immediate review by a user.Such a configuration is particularly advantageous for analyzing primaryand secondary opportunities quickly and efficiently.

The bubble data visualization of FIG. 7 may be advantageously configuredto provide immediate analytics generated from one or more modules in thesystem. Turning to FIG. 8A, an exemplary embodiment is provided whereopportunity bubble 701 is selected, which in turn launches analyticswindow 801 comprising graphical 803 and textual 802 representations ofthe underlying data. In this example, graphical representation 803comprises a chart illustrating a dollar value opportunity trend spanninga predetermined time period. Textual representation 802 comprises atable, indicating a site location (Ste), part name, ABC code, MOQ,multiple quantity value, reduction value and opportunity value, similarto the embodiments discussed above in connection with FIGS. 5A-F. Inchart 802, the component opportunities making up the total MOQopportunity may be simultaneously viewed to determine greater detailssurrounding the opportunity.

FIG. 8B illustrates an embodiment, where, if a system component isselected in section 802, a functionality window 804 is provided forassigning ownership, comments, entering actions and escalation tocomponents of 802. In this example, window 804 enables entry ofownership (“owner”) for a part number, and an “assigned by” andassignment date entry for each area (safety stock, MOQ). Comments may beentered into window 804 as shown, together with an action drop-down menuallowing automated action entries such as “not started”, “started”,“achieved”, “unachievable” and “in escalation”. For the escalationdrop-down menu, escalation system managers may be assigned via theinterface for further action.

As part of the embodiments disclosed herein, the system is furtherenabled to process and calculate risk(s), and various other factors andrelated factors, within supply chains, automatically and based on realtime data from a variety of sources. Generally speaking, supply chainrisks may emanate from geographic risk and attribute-based risk, amongothers. For geographic risks, manufacturing locations are registeredwithin the system for parts purchased so that when an area becomesvolatile because of socio-political, geographic, (macro-) economic,and/or weather-related disruption, related variables may be processed todetermine an effect on, or risk to, a supply chain.

For attribute risk, the system may be configured to calculate arisk-in-supply chain (risk attribute) value, where a risk attributevalue is based on a framework that analyzes various different riskcategories of the supply chain. FIG. 9 illustrates an exemplaryembodiment in which attributes 901-906, along with their respectivedescriptions 907, are assigned risk weight values 908 to calculate risk.In one embodiment, a total risk attribute score may be based on a 1-5scale, with 1 representing the least risk and 5 representing the mostrisk. The weights may be applied to stress attributes that may be moreimportant than others in calculating supply chain risk. Aftermultiplying a score in each category by the associated weight, scoresfor each part on an end product or assembly may be added up and dividedby the number of parts to determine a risk attribute score for eachassembly. Next, scores for each assembly for a customer are added up andthen divided by the number of assemblies to determine a risk attributescore for the customer.

In the embodiment of FIG. 9, the example illustrates six attributes:alternative sourcing 901, part change risk 902, part manufacturing risk903, lead time 904, spend leverage 905 and strategic status 906. Itshould be understood by those skilled in the art that additionalattributes or fewer attributes may be utilized by the system. Moreover,in the illustration, the attributes are weighted by a weightingalgorithm (applied at platform 307 or one of its associated apps) at36%, 19%, 9%, 19%, 19%, and 0%, respectively, although those skilled inthe art will appreciate that these weightings may be varied. Additionalattributes may comprise defects per million, lot return rate, correctiveaction count, inventory performance, environmental and regulatory items,security programs (e.g., Customs-Trade Partnership Against Terrorism(C-TPAT)), supplier financial status, supplier audit results andcomponent life cycle stages, among others. It is also worth noting thatattributes and weighting may be dependent upon data availability, i.e.,algorithms and selected attributes may be modified based onavailability, and the data selection and/or aspects of the appliedalgorithm may be controlled automatically and in real time by platform,and/or control may include or be exclusively indicated by manual inputsof data or aspects of the algorithms.

An exemplary risk attribute score detail is provided in the embodimentof FIG. 10. The embodiment of FIG. 10 illustrates an exemplary reportfor an assembly, where the report is based on each of the parts makingup the assembly and the associated risk attribute scores. In FIG. 10, apart level report is provided which provides additional detail by partto show risk attribute score specifics. Such a report may break out eachcategory score so that a user may see where, and to what extent, riskexists, and potential courses of action that may be taken to lessen therisk. As is illustrated in FIG. 10, the platform 307, and/or theindividual app, may receive primary data and generate therefromsecondary data, such as calculation of the risk attribute score. In theexample shown, the primary and secondary data used to generate the riskattribute score is the same as that of FIG. 9, although those skilled inthe art will appreciate that other primary and/or derived secondary datamay be used in supply chain risk calculations.

For example, and as illustrated in FIG. 11, if an alternative sourcingcategory contains a high risk attribute score, a user may investigatethe parts where the user requires purchase from only one manufacturer(“sourced parts”) which may be causing a high risk attribute score. Theuser may configure the system to enable or suggest other manufacturersas suppliers which will lower the risk attribute score by diversifyingthe supply base. Long lead times may increase a risk attribute score aswell. As such, a user may configure the system to communicate or ordersuppliers to lower lead times so that a manufacturer may react quickerto demand changes if the parts can be bought and received in a shortertime period. In either case, the risk attribute system module allows amanufacturer to be a proactive party in the chain and suggestalternative sourcing using an extended supply base to lessen the amountof sole source parts and reduce lead times for a customer. Of course, itis understood by those skilled in the art that the risk attributecalculations disclosed herein may be applied to any aspect or attributeof a supply chain system including parts, suppliers, manufacturers,geography, and so forth.

FIG. 11 additionally serves to illustrate that a weighting algorithm maybe employed with regard to primary and/or secondary data to arrive atrequested information, and further that such weighting algorithm may beadjusted over time, such as by platform 307, based on “learning” thatoccurs upon application of the algorithm. For example, if actual risk isrepeatedly indicated as higher than a generated risk attribute score,the weighting algorithm shown in FIG. 11 may be adjusted, and/or theprimary and secondary data that is used in the score calculation may bemodified, etc., in real time by the platform 307 (and/or by the appmaking use of the primary data, secondary data, and algorithms providedby the platform 307).

The risk attribute processing may subsequently be utilized by, and/ormay utilize, the SCM platform 307 to generate a heat map to visualizerisk attribute scores and their impact easily and quickly for a user. Incertain embodiments, heat maps may allow the display of multiplevariables, such as revenues and risk. In the illustrative example ofFIG. 12, a heat map 1200 is shown for a plurality of assemblies (Assy101-105), where each assembly is associated with one or more parts.Thus, assembly Assy 101 comprises five primary parts (“parts 201-205”),Assy 102 comprises “parts 206-210”, Assy 103 comprises “parts 211-215and so on.

In one embodiment, assemblies or parts with higher revenue over apredetermined time period (e.g., 90 days) are visualized with biggerboxes compared to assemblies and/or parts having lower revenue. Inaddition to size, the heat map may color code boxes to reflect riskattribute scores. The color codes may be configured to show green forlow risk, yellow for medium-low risk, orange for medium-high risk andred for high risk. As shown in FIG. 12, the risk attribute module may beconfigured to utilize a nested “heat map” format for inserting one setof heat maps into a higher level heat map. Thus, the heat maps for partsmay be simultaneously visualized with their associated assembly.

The figures discussed throughout provide illustrative screenshots ofsystem and system platform operation disclosed herein. The system screenlayout may contain a plurality of workspace and navigation areas. Across-function pane may be provided to present functional areas of abusiness which are included in the platform. User roles may be assignedin the system to control which functional areas may be made visible todifferent user groups. A tool pan may likewise be provided for toolsthat are available within each functional area of the business. Userroles may control which tools are visible to different user groups. Amain content pane may be provided as a main workspace of the platformwhich contains data and informational content. In embodiments, a newsfeed/main page/landing page may also be provided, such as for achronological timeline of events, facts, occurrences, status, risks, orthe like which may be particularly relevant to the user and/or relevantto the particular design/product/similar designs or products. “Pings”may contain alerts such as new critical shortages. In one embodiment, asocial data interface may be provided for real-time information fromsources such as Twitter. Through various interfaces, data may befiltered by geography, organization level or both.

Turning to FIG. 13, an exemplary screenshot is provided of a networkoptimizer module, which provides an active user interface for anexemplary global manufacturing footprint through a total landed costanalysis. The illustration shows a plurality of charts may be provided,such as total landed cost, freight and inventory financing expense.Product, market, mechanical sourcing and transportation costs mayfurther be provided, along with a “scenario” processing screen fordisplaying options for individual global markets (EU, IN, CN, US). Datapoints such as annual volume, freight in/out, value add, cost of capitaland other log expenses may be conveniently charted for presentationand/or other action. Economic indicators including currency, revenue andacro-economic indicators may be provided as well. More particularly,FIG. 13 illustrates that a supply chain may be optimized, such asthrough the use of analytics on large-volume data, and such as either atthe design phase or at the operational phase.

In short and as referenced throughout, various aspects of the supplychain, such as manufacturers, resellers, parts providers, and customersof parts and final products, may desire insight into the entirety of thesupply chain, or at least aspects thereof relevant to that contributorto the supply chain, rather than the known insight into only discreteaspects of the supply chain immediately relevant to that contributor tothe supply chain. Consequently, in certain embodiments software as aservice, such as with administrative limitations to access as will beunderstood by those skilled in the pertinent arts, may provide insightinto the overall supply chain, or more broad aspects thereof, than isknown in the art. This insight may be provided at both the design phasefor a product or supply chain, such as using analytics applied tovoluminous data of existing relevant supply chains as compared to thesupply chain of interest, and at the operational phase of the supplychain, such as to provide diagnostics on the supply chain.

For example, as product life cycles shrink, the supply chain maynecessarily need to become faster, lower in cost, lower and risk, andlower in failure rate, particularly in certain verticals, and this maybe reflected in competitive supply chains before it is executed in asupply chain of interest. In a more specific example, the ability todrive lead time for part acquisition to or near zero, such as throughthe use of 3D printing, if being performed by competitors, mustnecessarily be accounted for in the SCM or supply chain design for asupply chain of interest.

The foregoing aspects may require significant data-driven aspects of asoftware as a service for supply chain management. These aspects mayinclude, by way of non-limiting example: visibility into the entiresupply chain, including all companies, all resellers, all parts, and soon; analytics capable of analyzing big data in order to assess andbalance speed, cost, risk, likelihood of failure, recommendedreplacements and modifications, and to enable the waiting and balancingof the foregoing factors; and an ability to collaborate and dictateaction in the supply chain based on the analytics, such as wherein thecollaboration may be in the form of multi-point communications thatoccur automatically or manually, and such as wherein the actions may besubject to manual triggers or may trigger automatically. Accordingly, tomeet future supply chain analysis needs, the supply chain and many orall aspects thereof may be analytically productized, and this mayinclude the people and places that form part of the supply chain.

In order to optimize a supply chain at either the design or theoperational (i.e., redesign) phase, needs must be assessed andmodifications recommended as early as possible in the supply chainmanagement process. That is, necessary future changes in the supplychain to improve speed and costs, and to lower risk and failure rates,must be assessed in real time. This improves the operation andinter-operation of all automated aspects of the supply chain, i.e., themanagement of network resources, computing processing power, the needfor manual interaction with automated processes, including orderingprocesses, and the like. The draw on the aforementioned resources, amongother resources, is thus minimized through the use of certain of theembodiments.

By way of illustration and as shown in the attached figures and thediscussion below, a user may be enabled to create a design or redesign,such as by accessing a “create a new design” interface through the useof the disclosed aspects; although it will be appreciated that much ofthe discussion herein may be applied to an existing supply chain inorder to allow for optimization. This may be enabled by a supply chainanalytics engine 1703 within platform 307, as is shown in FIG. 14.Analytics engine may be, include, be included within, or be distinctfrom the supply chain analytics module 304 discussed above. Theanalytics engine 1703 may include an optimizer 1705 that may perform thefunctionality described in the Figures below, including optimization ofcomputing resources and networking related to SCM such as may beindicated by analysis by a rules engine 1707 that applies at least acomparator 1709 having therein comparative algorithms, such as thosediscussed below, to the large volume data 1711 from other supply chains,such as may include similar product verticals and dissimilar verticalsthat integrate similar parts, and to the supply chain of interest. Thelarge volume data may be, include, be included within, or be distinctfrom analytics data 322, discussed above.

As illustrated in FIGS. 15-26, the design of a supply chain to be runthrough comparator 1709 may include, by way of non-limiting example, alaunch date, a project name, various aspects of the design, and“preferences”. More particularly, preferences may be prioritizedobjectives for the design, such as wherein a user may rank the designobjectives. Accordingly, the wealth of data 1711 available to thedisclosed algorithms 1707 may be subjected to comparative analytics 1709(which may be anonymized), and the output from these analytics may beweighted, prioritized, or otherwise ranked in accordance with thepreferences input by the user.

Yet further, any data input by the user, such as current parts foraspects of a products design, may be subjected to the analytics 1709 inorder to make a completeness assessment in light of the enteredpreferences/priorities; that is, once data is entered to data store 1711by the user and preferences are entered by the user to the optimizer1705, automated searching may be performed, such as using a comparativealgorithm 1707 to the supply chains/part lists of similar product supplychains in the data store 1711, to assess the completeness of the designand the completeness of a supply chain that may be provided inaccordance with the design. If additional data or input is necessary,the disclosed algorithms may, such as in an automated manner and such asbased on pre-entered or user-input algorithms/rules to rules engine 1707or based on “learning” that occurs in accordance with previously inputlearning algorithms, make a qualitative and quantitative assessment ofthe user's input. In accordance with this assessment, additionalinformation may be requested from the user, or the user may beinstructed to obtain additional information to the analytics engine 1703as deemed necessary by the algorithms of the rules engine 1707.

In the known art, it is typical that research and development isperformed to design a product, and only thereafter is the supply chainthat will enable the creation of the product assessed. The discloseddesign for supply chain may integrate supply chain design with productdesign, as referenced above, such that product design may be tweaked ormodified as necessary in order to both optimize the final designedproduct and optimize the supply chain used to create the final product.This dual optimization provided by optimizer 1705 may indicatemodifications to both product design and the supply chain, as discussed,based on the priorities input by the user to the disclosed algorithms.By way of example, an input product design may be optimized using thedisclosed analytic algorithms, and the optimized product design bestsuited for an optimized supply chain may be output to the user based onthe user's input objectives/preferences.

Further provided to the user by optimizer 1705 may be a compare andcontrast of the user's original input design and its impact on thesupply chain, as compared to the optimized design and optimized supplychain. Thereby, the user may be enabled to interact, such as through thegraphical user interface shown and discussed herein and using knowncomputing peripherals, with the comparison in order to assess whetherthe automated modifications made in accordance with the analytics haveprovided an improvement or a detriment to the original product designand supply chain in light of the user's objectives. This comparison maybe provided by any known methods, including a side-by-side simultaneousgraphical illustration, such as may include user interactivity to enablea user to make modifications categorically within the side-by-sidecomparison to assess effects of those modifications on the side-by-sidecomparison; one or more of a series of split windows, hierarchical drilldown menus, or the like; or by any other known methodology.

As discussed, analytics are algorithmically applied, such as both basedon pre-stored, user input, and/or learning algorithms, in order toprovide the disclosed optimizations. These analytics may review avariety of variables indicated by data from data store 1711, includingbut not limited to lead time, alternate parts, product and partlifecycle, supplier and reseller alignments, product and part riskassessment, and the like, wherein each of the foregoing may be weightedor otherwise balanced in accordance with the user's input objectives, inorder to provide a recommended solution of the supply chain for theuser's input product based on all of the foregoing and other factors,such as manpower, geographic, and environmental effects, by way ofexample. Moreover, this recommended solution may typically stem not onlyfrom the user's input data, but from data gained from numerous similarand dissimilar products, the parts used to make those products, thesuppliers from which those parts are obtained, the manpower andgeography necessary to make those parts, and the like, all of which dataresides in data store 1711. Thereby, the disclosed analytics may provideoptimized suppliers, parts, source and assembly locations, and the like,which allows for a significant ramping of speed to market for an inputproduct design. Moreover, and as disclosed herein, a subscriber to theSaaS system set forth herein may be enabled to access very significantand or anonymized content in order to optimize a design in light of thesupply chain based on the performance of several, dozens, or hundreds ofother relevant supply chains.

It goes without saying that the analytics 1709 discussed herein,particularly when engaged in “learning”, may change as data isaccumulated by data store 1711 from particular verticals and/or acrosssimilar or dissimilar products. By way of example, to the extent thecomparison discussed herein is generated repeatedly to include aparticular part or parts, and the algorithmic estimation of performanceof the supply chain in light of that modification in the comparisonrepeatedly proves to underperform the estimated comparison, thedisclosed learning algorithm may assess that the particular part orparts common among the failed analytic estimations are the root cause ofthe failure. Thereby, the recommended use of that part or those parts infuture designs may be automatically minimized.

Needless to say, the aforementioned performance assessment may not onlybe globally performed by the analytics engine 1703 disclosed, but may beperformed on a case-by-case basis. That is, once the user selects asolution (such as may be recommended by the analytics engine in light ofthe input user preferences) the design for supply chain may convert to afull SCM platform 307 that tracks the progress of the selected solution,and which continues to compares to the initial user input, finalexecuted user solution, and or other available ongoing solutions toassess the performance of the selected solution.

As referenced above and discussed further below with reference to thefigures, a predetermined set of prioritized objectives may be madeavailable by the analytics engine 1703 to the user through a graphicaluser interface. By way of non-limiting example and as shown, theseobjectives (which may be prioritized by the user) may include price,preferred suppliers, risk, time to market, and flexibility, and/oruser-entered “free form” objectives. These objectives may be placed intoa simple sort for analytics purposes, i.e., they may be ranked accordingto user ranking input, they may be weighted based on learnings performedby the analytics algorithms, and/or they may be structured based onother information provided by the user, such as input goals orobjectives. It goes without saying that numerous additional objectivesto those shown and discussed throughout may be added without departingfrom the disclosed scope of the embodiments, and further that addedoptional objectives may be interdependent on rankings of the objectivesprovided by the user. That is, by way of non-limiting example, if theuser prioritizes risk as the number one supply chain design objective,the user may be provided with numerous other factors that either relateto or are subsets of risk, or that are generally important to users whoprioritize risk as a main objective according to data store 1711.

To the extent, such as rather than a simple rank sorting of objectives,weighting of objectives is applied by the disclosed analytics engine1703, such weighting may be obtained via a variety of methods. Theweightings may be, for example, algebraically obtained, such as whereina first ranked priority receives a 50% weight, a second ranked prioritya 20% weight, a third ranked priority a 15% weight, a fourth rankedpriority a 10% weight, and the last and fifth ranked priority a 5%weight. Additionally and alternatively, weightings may vary dependentupon the objectives provided by the user based on the users input goalsor requests, or the weightings may vary over time as data is accumulatedand made available to the analytics engine.

Also referenced throughout is a data completeness check. The datacompleteness checks discussed herein may vary from the simplistic to thecomplex. For example, data completeness may be complex and mayinterrelate to the input user objectives; that is, dependent on theobjective selected and/or ranked by the user, certain additional orparticularly complete data may be necessary. On the other hand, datacompleteness checks may be relatively simple, such as wherein input datafor a particular part or parts may be compared to a known part namingconvention, and if a letter is provided for a given part were a numberis expected by the system, the user input data may be flagged formodification. It goes without saying that the foregoing data error isprovided only by way of example, and it will be understood in light ofsuch examples that other data completeness checks and remedies may beperformed by the disclosed analytics engine 1703.

As such, data quality analytics may be applied by the analytics engine1703 to the data supplied by the user. For example, the aforementionedstandard naming conventions may allow for an automated comparison ofuser input to standard conventions, and, to the extent an error isassessed, comparison of that error to a list of common errors that aregenerally made in naming a part, a supplier, a location, or the like,may be performed. To the extent the subject error falls within the listof common errors, a correction may then automatically be made, or theuser may be asked if the user wishes to make a suggested modification tothe data. For example, a standard naming convention may indicate that aparticular supplier is referred to as “Supply AXE”, but the user mayhave entered “Supplier AX”. When this error is flagged, the enteredSupplier AX may be compared to a list of common errors, which list mayindicate that most users who enter Supplier AX actually intend to referto Supply AXE. Corrective action may then be taken, either automaticallyor with user permission.

Accordingly, “heuristic fuzzy logic searching” may be enabled by theanalytics engine 1703, in which a string search to a correspondingdatabase within or communicatively associated with the analytics engine1703 may be performed. Such heuristic fuzzy logic string searching mayallow for the application of heuristic fuzzy logic to data entry errors,which may allow for assessment of unique or common errors, and which mayallow for a request of user input if an error is suspected. Further,such heuristic fuzzy logic searching may allow for the generation of aconfidence threshold by the analytics engine 1703. As such, if theanalytics engine deems, based on the heuristic fuzzy logic search, thatthe engine is only 20% certain of what the user likely meant, acorrection may not automatically be made but instead the user may beasked if a correction to the heuristic fuzzy logic search results isdesired. Or the analytics engine may deem it undesirable to even makethe correction recommendation to the user due to the likelihood that thesuggested correction is not what the user desired based on the limited20% certainty threshold in the foregoing example.

Moreover and as referenced throughout, the analytics engine 1703 may“learn” as corrections are suggested and/or made. For example, if aconfidence threshold initially indicates that it is 20% likely that aparticular correction is what a user wants, but then nine consecutiveusers say yes that is a correction they wish to make, the confidencethreshold may successively increase, such as to a point where thecorrection to the data may be made automatically by the analytics enginewithout asking the user.

In relation to the foregoing supply chain design and optimization, FIG.15 illustrates a screen shot user interface suitable to access thesupply chain analytics engine 1703. As illustrated, the interface allowsthe user to engage in data manipulation to create, compare, and viewexisting designs or new designs in relation to performance of thosedesigns in the supply chain.

Yet more particularly, FIG. 16 illustrates various aspects of automateddesign assessment. For example, a design roadmap is illustrated. Whilethe design roadmap may initially be entered by the user, the analyticsengine 1703 may, either automatically or manually via user input, modifythe roadmap as needed to account for, by way of non-limiting example,availability of new parts, generation of new or additional data, ongoingmanufacturing performance, comparative performance of other designs, andthe like.

Further illustrated in FIG. 16 is an assessment of the data completenessfor the state of the design as entered by the user. That is, until thedata completeness reaches 100%, additional user input is indicated asneeded in order to complete the design sufficiently so that a fullassessment of supply chain performance of that design may be generatedby the engine 1703. Further illustrated are a plurality of interfaces tovarious different designs, such as may be for the same or differentproducts, and additionally the ability to compare those designs as tothe supply chain performance of each design.

By way of additional example, FIG. 17 illustrates design comparisons forsupply chain performance of various designs for the same or a similarproduct. As illustrated and given particular user inputs, differentdesign attributes—such as risk score, lead time, sourcing, supplieralignment, preferred suppliers, and part lifecycle—may be compared foreach of different designs such that the user may make judgmentsregarding those different designs as to which will perform best in lightof the prospective supply chain. Needless to say, stored data 1711 fromother supply chain designs may be used by the design engine to suggestdesign modifications or to make design recommendations, such as bycombining various different designs to arrive at an optimal design.

FIG. 18 illustrates that one or more designers may associate commentaryor documents with a particular design or designs. Further, designers mayexchange messages within the design window for a given design, as isalso illustrated in the example provided in FIG. 18.

A supply chain analysis for a single design is illustrated in theexample of FIG. 19. As shown, various design attributes may be assessedas to their respective impact on the supply chain. This may be reflectedfrom the supply chain analytics 1707, which may be uniquely associatedwith a particular design or vertical and which may, such as in a summaryformat, provide analysis of that design's likely performance withrespect to the supply chain. Of course, recommendations to improve thisperformance may also be made available from the assessments by theengine 1703.

FIG. 20 illustrates the exemplary creation of a new design. As shown,the design may have a name, description, and one or more documents, suchas drawings, images, part lists, and the like, associated therewith.FIG. 21 illustrates the association of milestones with a new design,such as via a drill down within the creation of a new design. As shown,milestones may be designed to have target dates, achieved dates,specific information, and comments associated therewith, by way ofnon-limiting example.

A new design may have preferences for the design and priorities for thedesign associated therewith, as discussed above and as illustrated inthe exemplary interface of FIG. 22. As shown, a user may be given a listof prospective objectives, or may input different objectives importantto that user, and may rank those objectives based on the priority to theuser. Further, the user may be enabled to select other supply chainpreferences, such as parts, preferred suppliers, preferred manufacturinglocations or manufacturers, and the like. By way of example, and asillustrated in FIG. 23, the user may also be enabled to upload or updatethe bill of materials, i.e., the parts, that are associated with a givenproduct design and compare the bill of materials across differentrevisions.

As referenced throughout, the various aspects of design creation mayhave associated therewith required data, and the design engine mayautomatically check this data for completeness of both the data and thesupply chain design. This is illustrated in the example of FIG. 24. Asshown, data completeness inadequacies or errors may be assessed by thedesign engine, and provided to the user.

As illustrated in FIG. 25, the provision of these data completenesserrors to the user may also include the performance, by the designengine, of a logical and/or heuristic fuzzy logic matching of the dataentered by the user to the existing data 1711 accessible to the designand analytics engine 1703. This heuristic fuzzy logic may allow for amatching of incorrectly entered part numbers to correct part numbers,misspelled or unknown objectives and preferences to available data (suchas prior known objectives and preferences), suggested corrections tomilestone dates, and the like. The logic applied by the matching enginefor a new design may or may not ask the user, such as subject to acertain correctness threshold, whether the user wishes to make a change.This application of logical matching may be controlled by one or morebusiness rules, such as may be associated with the rules engine 1707.

FIG. 26 illustrates an additional user interface to the rules engine1707. As shown, the user may be enabled to activate or deactivate someor all business rules typically associated with a new design foroptimized supply chain performance. Moreover, new business rules may beentered by the user to the rules engine 1707, and may be received andunderstood by the rules engine, such as by using semantical and/orlogical matching as discussed above.

FIG. 27 illustrates an exemplary health check screen shot that may beconfigured as part of a supply chain analytics engine 1703. As shown inthe main content pane of FIG. 27, the system may be configured toprocess and visualize various data points as a dashboard, where, in thisexample, demand, inventory and risk attribute scoring are provided in arelative data format (29%, 33% and 3.3, respectively). Flexibility andopportunity processing and visualization is provided in the example asbar charts, indicating time/value determinations over a variety ofpredetermined time periods (<6 weeks, <8 weeks, <12 weeks, >12 weeks).Opportunity processing and visualization generates a bar chartindicating lead time, safety stock and MOQ valuations determined in thesystem. Sourcing options may also be provided to determine sourcingarrangements for the visualized output.

The health check allows users to quickly assess the health of a customersupply chain over a plurality of key performance indicators (KPI). Fordemand, the demand KPI may focus on service level and/or deliveryperformance to a customer. The raw data may be processed via theplatform and subsequently displayed in the dashboard to indicate aservice level. For inventory, the inventory KPI may focus on theinventory position and breakdown. The dashboard indicator may display aproportion of excess and obsolete inventory versus a total inventory.For risk attribute, the risk attribute KPI displays a total supply chainrisk score for a customer as discussed herein. A sourcing KPI maydisplay a breakdown of BOM/parts/supplier ownership versus a customer. Aflexibility KPI may display a proportion of total demand flowing throughlead time thresholds. An opportunity KPI may display a breakdown ofpotential opportunities to improve flexibility or reduce cost in asupply chain.

FIG. 28 illustrates an exemplary screenshot of a node network diagram,similar to those disclosed above. FIG. 29 illustrates an exemplaryscreenshot of benchmark results that advantageously allow users toaccess one or more reference points for supply chain metrics and maycompare results against similar size and complexity customers in thesystem. Benchmarking may be performed using only that data accessible,pursuant to authorization, to that customer; using data across multipleclients of the manager of multiple customers; and/or using third partydata, such as may be purchased from third parties at networkedlocations. Further, in the particular example of FIG. 29, days ofsupply, risk attribute score and supply chain model processing resultbenchmarks are presented (41.56, 3.39 and 34%, respectively) againstsimilar benchmarks obtained from other customers (“DEMO CUST 1”),wherein it can be seen that the benchmarks are all abovesimilarly-situated customers (24.28, 3.38 and 7%), thus indicating thatpotential issues may need to be addressed in the system.

Turning to FIG. 30, an exemplary screen shot is provided for supplychain analytics which shows one example of processing and identifyingsupply chain opportunities. Here, an opportunity is defined in referenceto a lead time and MOQ, where specific data points may be processed andpresented for each attribute. The system may calculate and present aspecific opportunity value for lead time supply chain attributes($424,380), together with MOQ opportunity value ($951,931). As discussedabove in connection with FIG. 7, an opportunity bubble chart may besimultaneously presented, containing the same and/or related attributes(lead time, MOQ), for further analysis. As discussed above, theindividual bubbles of the bubble chart may be selected/rearranged topresent alternate and/or additional opportunity values, which may beautomatically recalculated and presented in the lead time and MOQ boxesshown in FIG. 30.

FIG. 31 illustrates an exemplary screen shot for status reports, whichprocess and display data points and metrics for analysis. In the exampleof FIG. 31, status reports may be generated for any metric includingsell thru, inventory, supply chain model, order status and servicelevel. In the example, sell thru status is displayed in dollars overpredetermined periods of time (e.g., week), where COGS and masterschedule metrics are displayed as a bar graph. The system may also beconfigured to overlay average COGS and MS averages onto the graph toprovide a quick analysis of system performance on these data point.

Continuing with FIG. 32, an exemplary inventory status report isillustrated, where inventory and days of supply are processed anddisplayed over a predetermined period of time (e.g., week). FIG. 33illustrates an exemplary supply chain model chart indicating apercentage of units, parts, assemblies, etc. that are in the supplychain model (inSCM) and out of the supply chain model (OutSCM) over apredetermined period of time (e.g., week).

Status reports for suppliers may be generated as shown in the exemplaryscreenshot of FIG. 34. The status reports may include, but are notlimited to, geographical impact reports and geographic risk attributescores by manufacturer. In the example of FIG. 34 an exemplarygeographical impact report is illustrated, showing impacts ofmanufacturers at given locations based on revenue and spend. Thegeographical impact report advantageously allows users to processrevenue impact and spend by supplier and/or geographic location. Suchinformation may be very useful in times of supply chain disruptions.

FIG. 35 illustrates an exemplary screenshot of a critical shortagessummary which may be obtained from status reports. In one embodiment,supplementary information and data relating to critical shortages may beobtained from 3rd party database sources (e.g., 110, 111 of FIG. 1)and/or may even be obtained from news feeds or social media as discussedabove.

Additional reports may be generated as shown in the exemplary screenshotof FIG. 36. Such information may be presented to allow for bettercontrol of those natural resources whose systematic exploitation andtrade in a context of conflict contribute to, benefit from or result inthe commission of serious violations of human rights, violations ofinternational humanitarian law or violations amounting to crimes underinternational law. Such information may be very useful when the originof supply chain materials is called into question.

The exemplary embodiments discussed herein, by virtue of the processingand networked nature of platform 307 and its associated apps, mayprovide typical data services, in conjunction with the specific featuresdiscussed herein. By way of non-limiting example, reports may be madeavailable, such as for download, and data outputs in variousformats/file types, and using various visualizations, may be available.Moreover, certain of the aspects discussed herein may be modified inmobile-device based embodiments, such as to ease processing needs and/orto fit modified displays.

In the foregoing Detailed Description, it can be seen that variousfeatures are grouped together in a single embodiment for the purpose ofstreamlining the disclosure. This method of disclosure is not to beinterpreted as reflecting an intention that the claimed embodimentsrequire more features than are expressly recited in each claim. Rather,as the following claims reflect, inventive subject matter lies in lessthan all features of a single disclosed embodiment. Thus the followingclaims are hereby incorporated into the Detailed Description, with eachclaim standing on its own as a separate embodiment.

What is claimed is:
 1. A supply chain design platform for designing asupply chain comprising a plurality of supply chain nodes, comprising: aplurality of data inputs capable of receiving primary hardware andsoftware data from at least one second supply chain accessible over acomputer network, and capable of receiving design data regarding aprospective at least one of the plurality of supply chain nodes, uponindication by at least one processor; a plurality of rules stored in atleast one memory element associated with the at least one processor andcapable of performing comparative operations on the primary hardwaredata, the software data, and the design data to produce secondary dataupon direction from the at least one processor; and a plurality of dataoutputs capable of: providing to a user interface of the secondary datacomprised of at least a substantially optimized one of the design forthe plurality of supply chain nodes based on the comparative applicationof at least ones of the plurality of rules; and providing the secondarydata to a user via the user interface.